PaDoc: Layout-Grounded Parallel Decoding for Document Parsing

• URL: http://arxiv.org/abs/2608.06146v1
• Date: Thu, 06 Aug 2026 15:16:18 GMT
• Title: PaDoc: Layout-Grounded Parallel Decoding for Document Parsing
• Abstract summary: Document serializes page layouts and regional contents into one autoregressive sequence.<n>We propose PaDoc, a layout-grounded that treats the predicted layout as a branching structure over a shared page representation.<n>On a 384-page subset and one A800, PaDoc is the fastest end-to-end at five levels, improving valid-page throughput by 67.4-118%.
• Score: 45.6402658645855
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: End-to-end document parsers provide a unified interface, but serialize page layouts and regional contents into one autoregressive sequence. This formulation forces independent regions onto a decoding path whose length grows with the total content, whereas crop-based two-stage parsers expose region-level parallelism at the cost of repeated visual prefills and fragmented page context. To retain full-page context while removing dependencies, we propose PaDoc, a layout-grounded parser that treats the predicted layout as a branching structure over a shared page representation. Under a region-sufficiency assumption, we derive a prefix-conditioned factorization in which the layout stream and regional content branches advance concurrently, reducing the decoding depth to the longest layout-content path. We realize this factorization within a single MLLM: packed variable-length ancestor attention preserves the visibility under standard next-token training, while masked parallel decoding creates branches that the evaluated vLLM backend serves as concurrent requests with cache-resident shared-prefix reuse. On OmniDocBench Full, PaDoc attains an Overall layout F1 of 91.1 and, among end-to-end parsers, a top-tier Overall score of 94.24 together with the best Text Edit (0.038) and Formula CDM (95.59). On a 384-page subset and one A800 GPU, it is the fastest end-to-end parser at five concurrency levels, improving valid-page throughput by 67.4-118% and reducing P95 latency by 39.2-54.9% relative to a same-backbone Sequential SFT baseline. Code is available at https://github.com/Longin-Yu/Padoc

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